Current digital cameras suffer from poor performance in low-light situations, when either a flash is not available, or is not beneficial. Exposure time can be increased to boost the number of photons reaching the sensor, but this solution typically reduces sharpness in the image if there is any motion in the scene or if the camera is not held absolutely steady. Digital cameras can also artificially boost the light intensity with a digital gain factor. The gain factor effectively scales upward the output codevalue for each pixel. The problem with this technique is that it amplifies noise as well as signal content. Low-light images typically have low signal-to-noise ratios, and the gain factor required to boost the images to acceptable intensity levels also causes unacceptable noise levels to be present in the images as well.
One method for controlling camera motion during an exposure is to force the exposure period to be very short, for example, 1/240th of a second. Such a short exposure is insufficient under many conditions, however, and results in an underexposed, noisy image.
Optical image stabilization has also been proposed to compensate for camera motion during an exposure. Optical image stabilization is typically accomplished during image capture by the use of a gyroscopic measurement accompanied by a controlled movement of a lens assembly mounted with lateral actuators. Prior art discloses a series of methods for gyroscopic measurement and lateral lens movement. Optical image stabilization is costly due to the need for gyroscopic measurements in multiple directions and the need for lateral actuators in the lens system likewise in multiple directions.
Alternatively, blur in the captured image can be reduced after image capture based on measurements of the camera motion that occurred during the image capture. Improved accuracy in the measurement of the camera motion will generally produce better results from the deblurring algorithm. Prior art on methods to track this camera motion can be divided into two general categories. One category is to track the camera's motion using a mechanical method, such as with gyroscopic measurements. This method can be costly due to the need for additional mechanical equipment in the image capture device. The other category for tracking camera motion is by deriving motion information from the captured image itself or from a series of captured images. These solutions are sometimes referred to as electronic image stabilization.
Such a method for correcting for camera motion during exposure includes capturing a burst of images, each at a fraction of the total desired exposure time. The multiple frames are aligned and combined to correct for camera motion and provide an output image with the desired total exposure time. The drawbacks of this method include sensor limitations on how quickly a frame of image data can be read off of the sensor, as well as memory costs for storing multiple images.
Another method for correcting for camera motion during exposure is the use of a blind deconvolution algorithm. Examples of blind deconvolution algorithms, such as the Lucy-Richardson algorithm, are well known to those skilled in the art, and can be used to reduce camera motion blur. The drawback of blind deconvolution algorithms is that they assume no a priori knowledge of the motion during the exposure, and their performance is therefore limited relative to techniques that have knowledge of the motion that occurred during exposure.
Another method for estimating camera motion during exposure involves the use of a first portion of a CMOS image sensor dedicated to collecting data to be used for motion estimation, while a second portion of the image sensor represents the image area. The first portion is usually a strip of rows from the top and/or bottom of the sensor area, and can be read multiple times during the exposure of the second part of the sensor. These readouts are used to estimate the camera motion during the exposure of the second portion of the sensor. The drawbacks of this method include the decrease in spatial resolution of the output image due to the sensor pixels dedicated to motion estimation. Additionally, strips of data along the top and/or bottom of the sensor often provide insufficient information for determining global motion.
Another method for estimating camera motion during exposure includes the simultaneous use of a separate sensor to capture data for motion estimation. Drawbacks of this method include the extra cost and space required for an extra sensor.
Accordingly, a need in the art exists for an improved process for determining and correcting for camera motion during exposure.